Establishment of Tumor Immune Microenvironment Classification Model to Select Patients Sensitive to Immunotherapy

医学 免疫疗法 肿瘤微环境 间质细胞 免疫系统 T细胞 癌症研究 免疫学 肿瘤科
作者
Dongchen Sun,Jiaqing Liu,Li Zhang
出处
期刊:Journal of Thoracic Oncology [Elsevier BV]
卷期号:18 (10): e111-e112 被引量:2
标识
DOI:10.1016/j.jtho.2023.07.003
摘要

We thank the Editor for the kind letter and valuable comments concerning our article entitled “Exploiting Tumor Immune Microenvironment to Predict Response to Immunotherapy Plus Chemotherapy in NSCLC.” All authors have seriously discussed all these comments. As was proposed in the article by Kim et al.,1Kim T.K. Vandsemb E.N. Herbst R.S. Chen L. Adaptive immune resistance at the tumour site: mechanisms and therapeutic opportunities.Nat Rev Drug Discov. 2022; 21: 529-540Crossref PubMed Scopus (84) Google Scholar advanced human cancer uses several mechanisms of adaptive immune resistance to escape from immune surveillance, including activation of the programmed cell death protein-1/programmed death-ligand 1 (PD-1/PD-L1) pathway and exclusion of tumor-infiltrating lymphocytes. In theory, PD-L1 expression and immune infiltration represent different aspects of the tumor microenvironment. Both are critical for understanding the immune response against the tumor, and their combined use could provide a more comprehensive view. Considering the highest predictive values of PD-L1 mRNA expression and Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) immune score, we chose them for our tumor immune microenvironment (TIME) classification model and effectively predicted survival benefits of immunotherapy in a randomized controlled NSCLC phase 3 study. In the letter, it was suggested that the collinearity between the ESTIMATE immune score and PD-L1 mRNA expression might affect the predictive performance of the TIME classification model. To confirm our conclusion, we also constructed other models on the basis of PD-L1 tumor proportional score (TPS) and CD8 T cells in Supplementary Figure 7A to H. In the model defined by PD-L1 mRNA and CD8+ T cell, consistent results were observed. Only tumors with high PD-L1 expression and high immune infiltration achieved the best survival outcomes. However, in the model, defined by PD-L1 TPS equal to 50% and ESTIMATE immune score, tumors with high immune scores achieved longer survival benefits no matter the levels of PD-L1 TPS expression. A reasonable explanation is that part of tumors with PD-L1 TPS less than 50% and high immune infiltration can also respond to immunotherapy. Therefore, although PD-L1 expression in tumor cells is less likely to be correlated with ESTIMATE score, we still believed our model on the basis of PD-L1 mRNA and ESTIMATE score represents the most predictive combination. In contrast, the correlations between PD-L1 expression and immune infiltration are complex. Previous studies proved that tumors with high tumor mutational burden had also increased the proportion of immune cells and total PD-L1–positive cells in NSCLC, achieving improved clinical outcomes of PD-L1 blockade.2Ricciuti B. Wang X. Alessi J.V. et al.Association of high tumor mutation burden in non-small cell lung cancers with increased immune infiltration and improved clinical outcomes of PD-L1 blockade across PD-L1 expression levels.JAMA Oncol. 2022; 8: 1160-1168Crossref PubMed Scopus (64) Google Scholar Other studies also suggested common targets of immunotherapies such as programmed cell death protein 1, PD-L1, LAG3, and CTLA4, which were up-regulated in the high-infiltration group.3Yan L. Song X. Yang G. Zou L. Zhu Y. Wang X. Identification and validation of immune infiltration phenotypes in laryngeal squamous cell carcinoma by integrative multi-omics analysis.Front Immunol. 2022; 13843467Google Scholar Considering the confounding associations between PD-L1 expression and immune infiltration, the collinearity between the ESTIMATE immune score and PD-L1 mRNA might not influence the predictive performance of our model strongly. According to the previous study, MHC class II molecules are predominantly expressed by professional antigen-presenting cells such as dendritic cells, B cells, and macrophages, and primarily present exogenously derived peptide antigens to CD4+ T cells.4Axelrod M.L. Cook R.S. Johnson D.B. Balko J.M. Biological consequences of MHC-II expression by tumor cells in cancer.Clin Cancer Res. 2019; 25: 2392-2402Crossref PubMed Scopus (208) Google Scholar In our research, we also proved that the MHC class II signature was enriched in patients with high immune infiltration no matter the level of PD-L1 expression. Therefore, we think that MHC class II antigen presentation is strongly correlated with the status of immune infiltration. To simplify our classification model for clinical practice, we did not include antigen-presenting pathway or CIITA expression in our model. Further studies are warranted to validate the efficacy of our model. In addition, establishing more accurate and interpretable predictive models by using comprehensive information from the TIME is a direction worth further exploring in the future. Dongchen Sun, Jiaqing Liu, Li Zhang: Conceptualization, Writing-original draft, Writing-review and editing. The study was funded by the Chinese National Natural Science Foundation Project (grant number 81872499) and China Postdoctoral Science Foundation (grant numbers BX2021393 and 2022M723634).

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
彩色的水绿完成签到,获得积分10
1秒前
明亮哈密瓜完成签到,获得积分10
1秒前
AcademicElite完成签到,获得积分10
2秒前
meng关注了科研通微信公众号
2秒前
2秒前
jzh完成签到,获得积分10
3秒前
Pepsi完成签到,获得积分10
4秒前
4秒前
arniu2008应助科研通管家采纳,获得20
4秒前
4秒前
香蕉觅云应助科研通管家采纳,获得10
4秒前
CHI发布了新的文献求助10
4秒前
华仔应助科研通管家采纳,获得10
4秒前
4秒前
4秒前
4秒前
大模型应助科研通管家采纳,获得10
4秒前
4秒前
pokexuejiao应助科研通管家采纳,获得10
4秒前
英姑应助NoGifTS采纳,获得10
5秒前
5秒前
qkdxh发布了新的文献求助10
5秒前
fanzi发布了新的文献求助10
5秒前
melens完成签到,获得积分10
5秒前
Cytheria完成签到,获得积分10
6秒前
万能图书馆应助向光采纳,获得10
6秒前
6秒前
科研通AI6.2应助李博士采纳,获得30
6秒前
酷波er应助李博士采纳,获得10
7秒前
传奇3应助LL采纳,获得10
7秒前
科研通AI6.4应助李博士采纳,获得10
7秒前
汉堡包应助李博士采纳,获得10
7秒前
科研通AI6.3应助李博士采纳,获得10
7秒前
科研通AI6.4应助李博士采纳,获得10
7秒前
丘比特应助李博士采纳,获得10
7秒前
科研通AI6.4应助李博士采纳,获得10
8秒前
molihuakai应助Seamily采纳,获得10
8秒前
汉堡包应助卡皮巴拉桑采纳,获得10
8秒前
科研通AI6.4应助李博士采纳,获得10
8秒前
务实的发带完成签到,获得积分10
8秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Resistance Spot Welding Dataset for Automobile Body-in-White Quality Analysis 748
日本現代怪異事典 副読本 700
悉尼大学博士学位论文,题目:Modelling and testing of one-sided stitched laminated composites. 作者:Kristopher P. Plain 650
Machine Learning for Asset Management and Pricing 600
Numerical analysis of the coupled atmosphere-ocean models (CAO II). II 600
Models for the coupled atmosphere and ocean 600
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7387187
求助须知:如何正确求助?哪些是违规求助? 8993743
关于积分的说明 19135319
捐赠科研通 7023958
什么是DOI,文献DOI怎么找? 3227996
关于科研通互助平台的介绍 2390684
邀请新用户注册赠送积分活动 2209083